Luke Metz

According to our database1, Luke Metz authored at least 38 papers between 2016 and 2023.

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Bibliography

2023
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
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Trans. Mach. Learn. Res., 2023

Noise-Reuse in Online Evolution Strategies.
CoRR, 2023

Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Transformer-Based Learned Optimization.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
General-Purpose In-Context Learning by Meta-Learning Transformers.
CoRR, 2022

VeLO: Training Versatile Learned Optimizers by Scaling Up.
CoRR, 2022

A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Discovered Policy Optimisation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies (Extended Abstract).
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers.
Proceedings of the Conference on Lifelong Learning Agents, 2022

Lyapunov Exponents for Diversity in Differentiable Games.
Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

2021
Gradients are Not All You Need.
CoRR, 2021

Training Learned Optimizers with Randomly Initialized Learned Optimizers.
CoRR, 2021

Reverse engineering learned optimizers reveals known and novel mechanisms.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies.
Proceedings of the 38th International Conference on Machine Learning, 2021

On Linear Identifiability of Learned Representations.
Proceedings of the 38th International Conference on Machine Learning, 2021

Learn2Hop: Learned Optimization on Rough Landscapes.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Parallel Training of Deep Networks with Local Updates.
CoRR, 2020

Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves.
CoRR, 2020

On Linear Identifiability of Learned Representations.
CoRR, 2020

Using a thousand optimization tasks to learn hyperparameter search strategies.
CoRR, 2020

Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Learning an Adaptive Learning Rate Schedule.
CoRR, 2019

Using learned optimizers to make models robust to input noise.
CoRR, 2019

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Understanding and correcting pathologies in the training of learned optimizers.
Proceedings of the 36th International Conference on Machine Learning, 2019

Guided evolutionary strategies: augmenting random search with surrogate gradients.
Proceedings of the 36th International Conference on Machine Learning, 2019

Meta-Learning Update Rules for Unsupervised Representation Learning.
Proceedings of the 7th International Conference on Learning Representations, 2019

Towards GAN Benchmarks Which Require Generalization.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Learned optimizers that outperform SGD on wall-clock and test loss.
CoRR, 2018

Guided evolutionary strategies: escaping the curse of dimensionality in random search.
CoRR, 2018

Learning Unsupervised Learning Rules.
CoRR, 2018

Learning to Learn Without Labels.
Proceedings of the 6th International Conference on Learning Representations, 2018

Adversarial Spheres.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Discrete Sequential Prediction of Continuous Actions for Deep RL.
CoRR, 2017

BEGAN: Boundary Equilibrium Generative Adversarial Networks.
CoRR, 2017

Unrolled Generative Adversarial Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.
Proceedings of the 4th International Conference on Learning Representations, 2016


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